Remote sensing image analysis by aggregation of segmentation-classification collaborative agents

- Two new approaches for collaborative remote sensing image analysis are presented. They both are based on a multi-paradigm framework which uses classification to guide a segmentation process.
- The proposed methods aggregate many mono-class extractors in order to make multi-class remote sensing image classification.
- Experiments show that the proposed methods give better results (both in terms of classification and segmentation) than a hybrid object-based approach as well as a deep learning approach, even if the training data is limited in quantity and quality.

In this article we present two different approaches for automatic remote sensing image interpretation which are based on a multi-paradigm collaborative framework which uses classification in order to guide the segmentation process. The first approach applies sequentially many one-vs-all class extractors in a manner inspired by cascading techniques in machine learning. The second approach applies many collaborating one-vs-all class extractors in parallel. We show that the collaboration of the segmentation and classification paradigms result in a remarkable reduction of segmentation errors but also in better object classification in comparison to a hybrid pixel-object approach as well as a deep learning approach.